Although the rise of Large Language Models (LLMs) in enterprise settings
brings new opportunities and capabilities, it also brings challenges, such as
the risk of generating inappropriate, biased, or misleading content that
violates regulations and can have legal concerns. To alleviate this, we present
"LLMGuard", a tool that monitors user interactions with an LLM application and
flags content against specific behaviours or conversation topics. To do this
robustly, LLMGuard employs an ensemble of detectors.

通过使用一组检测器，我们提出了 “LLMGuard”，这是一个监视用户与 LLM 应用程序交互并对内容进行标记的工具，以应对大型语言模型在企业环境中带来的新机遇和挑战。

LLMGuard：防范不安全 LLM 行为

LLMGuard: Guarding Against Unsafe LLM Behavior

Object detection models, which are widely used in various domains (such as
retail), have been shown to be vulnerable to adversarial attacks. Existing
methods for detecting adversarial attacks on object detectors have had
difficulty detecting new real-life attacks. We present X-Detect, a novel
adversarial patch detector that can: i) detect adversarial samples in real
time, allowing the defender to take preventive action; ii) provide explanations
for the alerts raised to support the defender's decision-making process, and
iii) handle unfamiliar threats in the form of new attacks. Given a new scene,
X-Detect uses an ensemble of explainable-by-design detectors that utilize
object extraction, scene manipulation, and feature transformation techniques to
determine whether an alert needs to be raised. X-Detect was evaluated in both
the physical and digital space using five different attack scenarios (including
adaptive attacks) and the COCO dataset and our new Superstore dataset. The
physical evaluation was performed using a smart shopping cart setup in
real-world settings and included 17 adversarial patch attacks recorded in 1,700
adversarial videos. The results showed that X-Detect outperforms the
state-of-the-art methods in distinguishing between benign and adversarial
scenes for all attack scenarios while maintaining a 0% FPR (no false alarms)
and providing actionable explanations for the alerts raised. A demo is
available.

本文介绍了一种新的抗干扰贴片检测方法 X-Detect，通过使用可解释的检测器的集成来检测物体检测模型的敌对攻击，并提供解释，以帮助防御者的决策过程。